This project forked from imistyrain/matconvnet-oneclick
MatConvNet使用指南
当前几乎所有主流的开源深度学习工具都做了一个非常强的假设：用户会自己去看源代码，也就是所谓的代码是自解释的。
其实真的也没什么难的，就是按照基本流程来就好了：
无外乎准备数据，设计网络结构，开始训练，最后是验证训练的结果。
MatConvNet示例数据都是很常用的，mnist、cifar等有自己的存储格式，按照它给的定义解析出来就好了。ImageNet太大，100多G，小课题组没有强大的装备不可能去实现，也没那个必要。
这里给出的示例相对而言还是比较科学的，把每个类别的数据放在同一个文件夹下，然后依次读取，并划分训练集和验证集，只不过还缺少一个随机打乱数据顺序的过程。
网络结构可以参考已有的结构，稍加改动，训练就一句代码搞定。
验证训练效果的话也可以参考已有的例子实现。
具体参见 MatConvNet使用指南.pdf
相关： mxnet训练自己的数据
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